CN104462856B - Ship Conflict Early Warning Method - Google Patents
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- 238000012545 processing Methods 0.000 claims abstract description 11
- 238000012544 monitoring process Methods 0.000 claims abstract description 6
- 238000012549 training Methods 0.000 claims abstract description 4
- 230000009466 transformation Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 3
- 230000002068 genetic effect Effects 0.000 claims description 3
- 238000005096 rolling process Methods 0.000 abstract description 4
- 230000006399 behavior Effects 0.000 abstract 1
- 238000007781 pre-processing Methods 0.000 abstract 1
- 230000007704 transition Effects 0.000 description 4
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- 230000032683 aging Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
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Abstract
The invention relates to a ship conflict early warning method, which comprises the following steps that firstly, real-time and historical position information of a ship is obtained through a sea surface radar and is subjected to preliminary processing; the method comprises the steps of preprocessing ship track data at each sampling moment, clustering the ship track data at each sampling moment, performing parameter training on the ship track data at each sampling moment by using a hidden Markov model, acquiring a hidden state q corresponding to an observation value at the current moment by adopting a Viterbi algorithm according to parameters of the hidden Markov model at each sampling moment, setting a prediction time domain W at each sampling moment, acquiring a position prediction value O of a ship at a future time period based on the hidden state q at the current moment of the ship, monitoring dynamic behaviors of the ship by establishing a safety rule set, and sending alarm information in time. The method can predict the ship track in real time by rolling, effectively warn sea area conflict and improve the safety of marine traffic.
Description
Technical field
The present invention relates to a kind of marine site traffic control method, more particularly to a kind of ship conflict based on Rolling Planning strategy
Method for early warning.
Background technology
With the fast development of global shipping business, the traffic in the busy marine site in part is further crowded.It is close in vessel traffic flow
Collection complexity marine site, still the regulation model allocated at artificial interval has been combined not for the collision scenario between ship using sail plan
Adapt to the fast development of shipping business.To ensure the personal distance between ship, implement effective conflict early warning and just handed over as marine site
The emphasis of siphunculus system work.Ship conflict early warning is a key technology in navigational field, safe and efficient early warning scheme pair
It is significant in increasing marine site ship flow and ensuring that sea-freight is safe.
In order to improve the efficiency of navigation of ship, marine radar automatic plotter has been widely applied to ship monitor at present
In collision prevention, the equipment provides reference frame by extracting ship relevant information for the judgement of collision scenario between ship.And ship
Conflict early warning be based on the basis of the prediction to ship track, in ship real navigation, by meteorological condition, navigation equipment with
And the influence of the various factors such as driver's operation, its running status often not exclusively belongs to a certain specific motion state, therefore
Prediction and ship conflict early warning at present to ship track is without accurate scheme.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of preferable ship conflict method for early warning of robustness, this method
Ship trajectory predictions precision is higher, the ship conflict accuracy of early warning and ageing preferable.
Realize that the technical scheme of the object of the invention is to provide a kind of ship conflict method for early warning, including following several steps：
1. the real-time and historical position information of ship is obtained by sea radar, the positional information of each ship is discrete two-dimensional
Position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'], by application wavelet transformation theory to original
Discrete two-dimensional position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'] preliminary treatment is carried out, so as to obtain
Take the denoising discrete two-dimensional position sequence x=[x of ship1,x2,...,xn] and y=[y1,y2,...,yn]；
2. ship track data is pre-processed in each sampling instant, according to acquired ship denoising discrete two-dimensional position
Sequence x=[x1,x2,...,xn] and y=[y1,y2,...,yn], processing is carried out to it using first-order difference method and obtains new ship
Oceangoing ship discrete location sequence △ x=[△ x1,△x2,...,△xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xi=
xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence △ new after processing
X and △ y, by setting cluster number M', are clustered to it respectively using genetic algorithm for clustering；
4. parameter training is carried out using HMM to ship track data in each sampling instant, by that will locate
Vessel motion track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number
N and parameter update period τ ', are rolled according to T' nearest position detection value and using B-W algorithms and obtain newest Hidden Markov
Model parameter λ '；
5. current time sight is obtained using Viterbi algorithm according to HMM parameter in each sampling instant
Hidden state q corresponding to measured value；
6. in each sampling instant, by setting prediction time domain W, the hidden state q based on ship current time, future is obtained
The position prediction value O of period ship, speculates to the track of ship in future time period so as to be rolled in each sampling instant；
7. in each sampling instant, the ship of running status and setting based on each ship needs to meet when running in marine site
Safety regulation collection, when the situation for being possible to occur violating safety regulation between ship, to its dynamic behaviour implementing monitoring and be
Maritime traffic control centre provides timely warning information.
Further, the step 1. in, by application wavelet transformation theory to original discrete two-dimensional position sequence x'=
[x1',x2',...,xn'] and y'=[y1',y2',...,yn'] preliminary treatment is carried out, so as to obtain the denoising discrete two-dimensional of ship
Position sequence x=[x1,x2,...,xn] and y=[y1,y2,...,yn]：For given original two dimensional sequence data x'=
[x1',x2',...,xn'], it is carried out approximately respectively using the linear representation of following form：
Wherein：
F'(x' the function expression to being obtained after data smoothing processing) is represented, ψ (x') represents female ripple, and δ, J and K are small
Wave conversion constant, ψJ,K(x') transition form of female ripple, c are representedJ,KRepresent the function coefficients obtained by wavelet transform procedure, its body
Wavelet ψ is showedJ,K(x') to the weight size of whole approximation to function, if this coefficient very little, then it means wavelet ψJ,K(x')
Weight it is also smaller, thus can be on the premise of not influence function key property, by wavelet ψ during approximation to functionJ,K
(x') remove；In real data processing procedure, implement " threshold transition " by given threshold χ, work as cJ,K<During χ, c is setJ,K
=0；The selection of threshold function table uses the following two kinds mode：
With
For y'=[y1',y2',...,yn'], denoising is also carried out using the above method.
Further, the step 4. it is middle determine flight path HMM parameter lambda '=the process of (π, A, B) is as follows：
4.1) variable assigns initial value：Variable π is given using being uniformly distributedi, aijAnd bj(ok) assign initial valueWithAnd
It is set to meet constraints：WithThus
To λ0=(π0,A0,B0), wherein okRepresent a certain aobvious observation, π0、A0And B0It is by element respectivelyWithThe square of composition
Battle array, makes parameter l=0, o=(ot-T'+1,...,ot-1,ot) be current time t before T' historical position observation；
4.2) E-M algorithms are performed：
4.2.1) E- steps：By λlCalculate ξe(i, j) and γe(si)；
VariableSo
Wherein s represents a certain hidden state；
4.2.2) M- steps：WithEstimate respectively
Count πi, aijAnd bj(ok) and thus obtain λl+1；
4.2.3) circulate：L=l+1, repeats E- steps and M- steps, until πi、aijAnd bj(ok) convergence, i.e.,
|P(o|λl+1)-P(o|λl)|<ε, wherein parameter ε=0.00001, return to step 4.2.4)；
4.2.4)：Make λ '=λl+1, algorithm terminates.
Further, 5. the middle iterative process for determining the optimal hidden status switch of ship track is as follows for the step：
5.1) variable assigns initial value：Make g=2, βT'(si(the s of)=1i∈ S), δ1(si)=πibi(o1), ψ1(si)=0, wherein,
, wherein variable ψg(sj) represent to make variable δg-1(si)aijTake the hidden state s of ship track of maximumi, parameter S represents
The set of hidden state；
5.2) recursive process：
5.3) moment updates：G=g+1 is made, if g≤T', return to step 5.2), otherwise iteration ends and go to step
5.4)；
5.4)Go to step 5.5)；
5.5) optimal hidden status switch is obtained：
5.5.1) variable assigns initial value：Make g=T'-1；
5.5.2) backward recursion：
5.5.3) moment updates：G=g-1 is made, if g >=1, return to step 5.5.2), otherwise terminate.
Further, the step 3. in, cluster number M' value is 4.
Further, the step 4. in, state number N value is 3, and it is 30 seconds that parameter, which updates period τ ', and T' is 10.
Further, the step 6. in, prediction time domain W is 300 seconds.
Further, the step 7. in carry to the dynamic behaviour implementing monitoring of each ship and for maritime traffic control centre
Detailed process for timely warning information is as follows：
7.1) the safety regulation collection D that need to be met when construction ship is run in marine sitemr(t)≥Dmin, wherein Dmr(t) represent
Any two ship m and ship r are in the distance of t, DminRepresent the minimum safe distance between ship；
7.2) according to the sampling time, the observer Λ by the continuous running status of ship to discrete sampling state is set up:Γ→
Ξ, wherein Γ represent the continuous running status of ship, and Ξ represents the discrete sampling state of ship；
7.3) as ship m and r observer ΛmAnd ΛrDiscrete observation numerical value ΞmAnd ΞrShow the vector not in t
When safety regulation is concentrated, i.e. relational expression Dmr(t)≥DminWhen invalid, alarm letter is sent to maritime traffic control centre at once
Breath.
The present invention has positive effect：(1) present invention during the real-time estimate of ship track, incorporated it is random because
The influence of element, the rolling track prediction scheme used can extract the changing condition of extraneous enchancement factor in time, improve ship
The accuracy of oceangoing ship trajectory predictions.
(2) present invention is based on different performance index, and its ship track real-time estimate result can be in the presence of the multiple of conflict
Ship, which is provided, frees trajectory planning scheme, improves the economy of vessel motion and the utilization rate of sea area resources.
(3) the early warning effect that the present invention conflicts to ship preferably, can effectively, accurately and real-time predict the track of ship simultaneously
Ship conflict is predicted, the security of marine site traffic is effectively improved.
Brief description of the drawings
Fig. 1 is the short-term Track Pick-up schematic flow sheet of vessel motion in the present invention；
Fig. 2 is the vessel motion situation monitoring schematic flow sheet in the present invention.
Embodiment
(embodiment 1)
See Fig. 1, the ship conflict method for early warning of the present embodiment includes following several steps：
1. the real-time and historical position information of ship is obtained by sea radar, the positional information of each ship is discrete two-dimensional
Position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'], by application wavelet transformation theory to original
Discrete two-dimensional position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'] preliminary treatment is carried out, so as to obtain
Take the denoising discrete two-dimensional position sequence x=[x of ship1,x2,...,xn] and y=[y1,y2,...,yn], it is original for what is given
Two-dimensional sequence data x'=[x1',x2',...,xn'], it is carried out approximately respectively using the linear representation of following form：
Wherein：
F'(x' the function expression to being obtained after data smoothing processing) is represented, ψ (x') represents female ripple, and δ, J and K are small
Wave conversion constant, ψJ,K(x') transition form of female ripple, c are representedJ,KRepresent the function coefficients obtained by wavelet transform procedure, its body
Wavelet ψ is showedJ,K(x') to the weight size of whole approximation to function, if this coefficient very little, then it means wavelet ψJ,K(x')
Weight it is also smaller, thus can be on the premise of not influence function key property, by wavelet ψ during approximation to functionJ,K
(x') remove；In real data processing procedure, implement " threshold transition " by given threshold χ, work as cJ,K<During χ, c is setJ,K
=0；The selection of threshold function table uses the following two kinds mode：
With
For y'=[y1',y2',...,yn'], denoising is also carried out using the above method；
2. ship track data is pre-processed in each sampling instant, according to acquired ship denoising discrete two-dimensional position
Sequence x=[x1,x2,...,xn] and y=[y1,y2,...,yn], processing is carried out to it using first-order difference method and obtains new ship
Oceangoing ship discrete location sequence △ x=[△ x1,△x2,...,△xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xi=
xi+1-xi,△yi=yi+1-yi(i=1,2 ..., n-1)；
3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence △ new after processing
X and △ y, by setting cluster number M', are clustered to it respectively using genetic algorithm for clustering；
4. parameter training is carried out using HMM to ship track data in each sampling instant, by that will locate
Vessel motion track data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden state number
N and parameter update period τ ', are rolled according to T' nearest position detection value and using B-W algorithms and obtain newest Hidden Markov
Model parameter λ '；Determine flight path HMM parameter lambda '=the process of (π, A, B) is as follows：
4.1) variable assigns initial value：Variable π is given using being uniformly distributedi, aijAnd bj(ok) assign initial valueWithAnd
It is set to meet constraints：WithThus
To λ0=(π0,A0,B0), wherein okRepresent a certain aobvious observation, π0、A0And B0It is by element respectivelyWithThe square of composition
Battle array, makes parameter l=0, o=(ot-T'+1,...,ot-1,ot) be current time t before T' historical position observation；
4.2) E-M algorithms are performed：
4.2.1) E- steps：By λlCalculate ξe(i, j) and γe(si)；
VariableSo
Wherein s represents a certain hidden state；
4.2.2) M- steps：WithEstimate respectively
Count πi, aijAnd bj(ok) and thus obtain λl+1；
4.2.3) circulate：L=l+1, repeats E- steps and M- steps, until πi、aijAnd bj(ok) convergence, i.e.,
|P(o|λl+1)-P(o|λl)|<ε, wherein parameter ε=0.00001, return to step 4.2.4)；
4.2.4)：Make λ '=λl+1, algorithm terminates.
5. current time sight is obtained using Viterbi algorithm according to HMM parameter in each sampling instant
Hidden state q corresponding to measured value：
5.1) variable assigns initial value：Make g=2, βT'(si(the s of)=1i∈ S), δ1(si)=πibi(o1), ψ1(si)=0, wherein,
, wherein variable ψg(sj) represent to make variable δg-1(si)aijTake the hidden state s of ship track of maximumi, parameter S represents
The set of hidden state；
5.2) recursive process：
5.3) moment updates：G=g+1 is made, if g≤T', return to step 5.2), otherwise iteration ends and go to step
5.4)；
5.4)Go to step 5.5)；
5.5) optimal hidden status switch is obtained：
5.5.1) variable assigns initial value：Make g=T'-1；
5.5.2) backward recursion：
5.5.3) moment updates：G=g-1 is made, if g >=1, return to step 5.5.2), otherwise terminate..
6. in each sampling instant, by setting prediction time domain W, the hidden state q based on ship current time, future is obtained
The position prediction value O of period ship.
Above-mentioned cluster number M' value is 4, state number N value is 3, and parameter updated period τ ' for 30 seconds, and T' is 10,
It is 300 seconds to predict time domain W.
(application examples, navigation traffic control method)
The navigation traffic control method of the present embodiment includes following several steps：
Step A, the ship obtained according to embodiment 1 conflict method for early warning obtain what ship speculated in each sampling instant
The track of ship in future time period；
Step B, in each sampling instant, based on the current running status of ship and historical position observation sequence, obtain sea
The numerical value of domain wind field variable, its detailed process is as follows：
B.1 the stop position of ship) is set as track reference coordinate origin；
B.2) when ship is in straight running condition and at the uniform velocity turning running status, marine site wind field linear filtering mould is built
Type；
B.3 the numerical value of wind field variable) is obtained according to constructed Filtering Model.
Step C, in each sampling instant, the need when ship of running status and setting based on each ship run in marine site
The safety regulation collection of satisfaction, when the situation for being possible to occur violating safety regulation between ship, to its dynamic behaviour implementing monitoring
And provide timely warning information for maritime traffic control centre；
Step D, when warning information occurs, on the premise of ship physical property and marine site traffic rules is met, pass through
Set optimizing index function and incorporate wind field variable value, ship collision avoidance track is entered using Model Predictive Control Theory method
Row Rolling Planning, and program results is transferred to each ship execution, its detailed process is as follows：
D.1) the termination reference point locations P of setting ship collision avoidance trajectory planning, collision avoidance policy control time domain Θ, trajectory predictions
Time domain γ；
D.2 on the premise of) being set in given optimizing index function, based on cooperative collision avoidance trajectory planning thought, by
The different weight of each ship imparting and the real-time wind field variable filtering numerical value of involvement, obtain the collision avoidance track of each ship and keep away
Hit control strategy and program results is transferred to each ship and perform, and each ship only implements its first in Rolling Planning interval
Optimal Control Strategy；
D.3) in next sampling instant, repeat step is D.2 until each ship reaches it and frees terminal.
Above-mentioned termination reference point locations P is set as next navigation channel point of vessel position conflict point, during collision avoidance policy control
Domain Θ is 300 seconds；Trajectory predictions time domain γ is 300 seconds.
7. Fig. 2 is seen, in each sampling instant, when the ship of running status and setting based on each ship is run in marine site
The safety regulation collection that need to be met, when the situation for being possible to occur violating safety regulation between ship, implements to supervise to its dynamic behaviour
Control and provide timely warning information for maritime traffic control centre, its detailed process is as follows：
7.1) the safety regulation collection D that need to be met when construction ship is run in marine sitemr(t)≥Dmin, wherein Dmr(t) represent
Any two ship m and ship r are in the distance of t, DminRepresent the minimum safe distance between ship；
7.2) according to the sampling time, the observer Λ by the continuous running status of ship to discrete sampling state is set up:Γ→
Ξ, wherein Γ represent the continuous running status of ship, and Ξ represents the discrete sampling state of ship；
7.3) as ship m and r observer ΛmAnd ΛrDiscrete observation numerical value ΞmAnd ΞrShow the vector not in t
When safety regulation is concentrated, i.e. relational expression Dmr(t)≥DminWhen invalid, alarm letter is sent to maritime traffic control centre at once
Breath.
Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to the present invention
The restriction of embodiment.For those of ordinary skill in the field, it can also be made on the basis of the above description
Its various forms of changes or variation.There is no necessity and possibility to exhaust all the enbodiments.And these belong to this hair
Among the obvious change or variation that bright spirit is extended out are still in protection scope of the present invention.
Claims (1)
- The method for early warning 1. a kind of ship conflicts, it is characterised in that including following several steps：1. the real-time and historical position information of ship is obtained by sea radar, the positional information of each ship is discrete two-dimensional position Sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'], by application wavelet transformation theory to original discrete Two-dimensional position sequence x'=[x1',x2',...,xn'] and y'=[y1',y2',...,yn'] preliminary treatment is carried out, so as to obtain ship The denoising discrete two-dimensional position sequence x=[x of oceangoing ship1,x2,...,xn] and y=[y1,y2,...,yn]；2. ship track data is pre-processed in each sampling instant, according to acquired ship denoising discrete two-dimensional position sequence X=[x1,x2,...,xn] and y=[y1,y2,...,yn], using first-order difference method it is carried out processing obtain new ship from Dissipate position sequence △ x=[△ x1,△x2,...,△xn-1] and △ y=[△ y1,△y2,...,△yn-1], wherein △ xi=xi+1- xi,△yi=yi+1-yi, i=1,2 ..., n-1；3. ship track data is clustered in each sampling instant, to ship discrete two-dimensional position sequence △ x new after processing and △ y, by setting cluster number M', are clustered to it respectively using genetic algorithm for clustering；4. parameter training is carried out using HMM to ship track data in each sampling instant, after it will handle Vessel motion track data △ x and △ y be considered as the aobvious observations of hidden Markov models, by set hidden state number N and Parameter updates period τ ', is rolled according to T' nearest position detection value and using B-W algorithms and obtains newest Hidden Markov mould Shape parameter λ '；5. current time observation is obtained using Viterbi algorithm according to HMM parameter in each sampling instant Corresponding hidden state q；6. in each sampling instant, by setting prediction time domain W, the hidden state q based on ship current time, future time period is obtained The position prediction value O of ship, speculates to the track of ship in future time period so as to be rolled in each sampling instant；7. in each sampling instant, the peace that the ship of running status and setting based on each ship need to be met when being run in marine site Full rule set, when the situation for being possible to occur violating safety regulation between ship, to its dynamic behaviour implementing monitoring and be sea Traffic control center provides timely warning information.
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| CN201710575751.XA CN107480424A (en) | 2014-12-30 | 2014-12-30 | Ship Conflict Early Warning Method Based on Rolling Planning Strategy |
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| CN109255492A (en) * | 2015-03-31 | 2019-01-22 | 江苏理工学院 | Subway track real-time prediction method based on robust strategy |
| CN106777833A (en) * | 2015-03-31 | 2017-05-31 | 江苏理工学院 | Subway traffic flow optimization control method based on robust strategy |
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| CN107776613A (en) * | 2015-03-31 | 2018-03-09 | 江苏理工学院 | Subway traffic flow optimization control system |
| CN106938655A (en) * | 2015-03-31 | 2017-07-11 | 江苏理工学院 | Subway traffic conflict early warning method |
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| CN105137971B (en) * | 2015-08-03 | 2018-07-06 | 大连海事大学 | A kind of method that auxiliary ship station carries out Decision of Collision Avoidance |
| CN107577230B (en) * | 2017-08-16 | 2020-01-14 | 武汉理工大学 | Intelligent collision avoidance system for unmanned ship |
| CN108334905A (en) * | 2018-02-08 | 2018-07-27 | 中电科技（合肥）博微信息发展有限责任公司 | Ship action trail recognition methods |
| CN110333726A (en) * | 2019-07-29 | 2019-10-15 | 武汉理工大学 | A kind of safety of ship DAS (Driver Assistant System) based on ship motion prediction |
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| CN104462856A (en) | 2015-03-25 |
| CN107480424A (en) | 2017-12-15 |
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